The study of human behavior in urban environment is an important field in city sciences. However, it's usually not easy to collect behavior data especially in newly developed area. Recently, generative agents provide a way to simulate human behaviors without data using llms. But these generative methods still face the challenge of reliability, as they may not always produce consistent or realistic behavior simulations.
To address this, a hybrid approach is proposed, combining Graph Retrieval-Augmented Generation (Graph RAG) with LLMs. By integrating graph-based retrieval, the model aims to enhance the contextual understanding and accuracy of the generated behaviors with a small amount of data, making the simulations more reliable and reflective of real-world dynamics.